Computer-Aided Drug Design and Discovery

Explore how molecular structures are represented on computers for cheminformatics, covering 2d and 3d representations, nomenclature, iupac, and surface models, and how machine readable formats enable the drug discovery pipeline.

Explore how to represent molecular structures on a computer using sdf and smiles formats, including atom coordinates, bond types, and the connectivity table for accurate 2d and 3d representations.

Explore molecular file formats in computer-aided drug design: smiles for compact 2D connectivity, sdf and Tripos Malta with optional atom charges, and pdb for macromolecules and autodock docking data.

Explore how to edit, modify, and build molecular structures and perform similarity, substructure, and superstructure searches to identify related compounds for virtual screening and docking in drug design.

Explore how molecular representation drives similarity measures and downstream analysis in cheminformatics, and why choosing the right representation matters. See a practical example drawing structures in different formats.

Learn MarvinSketch basics, including installation with a free student license, draw and edit molecules, and save in multiple formats for 2D and 3D use, and check structures and basic properties.

Learn to perform full structure search, similarity search, substructure, and superstructure in MarvinSketch via the file option, explore smiles, and export structures for other software.

Explore compound libraries as a path to drug discovery by navigating chemical space, assessing binding affinity and hydrogen bonds, balancing hydrophobicity and hydrophilicity, and avoiding toxicity and cross reactivity.

Explore the chemical search space around molecular properties like pKa and logP by iteratively modifying reference molecules, such as carbetocin or antibacterial drugs, to discover novel compounds.

Combine libraries of stored chemicals to expand screening space in high throughput and virtual screening, targeting kinases for cancer with molecules that fit binding sites.

Maximize use of available chemical space to boost the odds of finding a good molecular structure. Employ high throughput or virtual screening, and enable de novo synthesis of novel compounds.

Explore library types in drug discovery, focusing on focused libraries from PubChem and Zinc, and natural product databases such as Coconut and Dr Duke's phytochemical data, with qsar guidance.

Identify fragment libraries of small fragments with weak affinity, and apply fragmentation and virtual fragmentation using tools like Super Structure search and EML frag to design molecules from Life Chemicals.

Combinatorial libraries explore a vast chemical space, enabling around 10 to 120 compounds without restrictions, while computer libraries search this space by modifying existing compounds to find better molecules.

Explore reaction-based enumeration to build combinatorial libraries using scaffolds, building blocks, and optional linkers, ensuring chemical feasibility and generating diverse molecules for drug discovery.

Explore the virtual library design workflow in drug discovery, part nine, from databases, fragmentation, and enumeration to virtual screening of millions of molecules for drug-like quality.

Explore PubChem, the NCBI freely accessible database of chemical information, and learn to search by name, formula, or structure, view compound records, and download 3D conformers for docking.

Explore PubChem for celecoxib: download all 42 related forms as 2D or 3D SDF, perform 2D and 3D similarity, substructure and superstructure searches, and build focused libraries.

Explore PubChem and other databases to download molecular structures and build libraries for in silico docking and screening against targets like EGFR and COX-2.

Explore how to generate combinatorial libraries using the SMI lib software, inputting scaffolds, linkers, and building blocks in smiles format to enumerate and save libraries as SDF.

Learn how virtual screening uses in silico scoring, ranking, and filters to triage compound libraries, applying druglikeness, ADME properties, and docking to select promising candidates.

Lipinski's rule of five defines drug likeness, requiring weight under 500, log p under 5, and donors under 5 with acceptors under 10 to improve absorption and lead discovery.

Explore pharmacophore queries, USAR, and other screening methods alongside structure-based, similarity, and machine learning approaches in virtual screening to filter libraries from millions to a few high-potential compounds.

Learn to use a drug likeness tool to screen sdf-uploaded molecules against Lipinski's rule of five and other filters, then calculate properties and save Lipinski-compliant results.

Understand how absorption, distribution, metabolism, and excretion shape drug pharmacokinetics, influenced by size, lipophilicity, solubility, ionization, permeability, active transport, and toxicity; virtual screening relevance is discussed.

Explore how solubility, log p, pKa, and other ADME properties govern absorption, distribution, and elimination, and how online tools and predictive models assess toxicity, druglikeness, and synthetic accessibility for virtual screening.

Discover the SwissADME tool to compute physicochemical and ADME properties, evaluate drug-likeness and bioavailability using multiple filters, and interpret boiled egg plot to compare molecules via smiles or Marvin Sketch.

Identify the biological origin of disease and the potential target for therapeutic intervention as the first step in drug discovery, using diverse approaches and technologies from research and early development.

Identify and validate drug targets by analyzing binding sites on proteins and DNA, understanding conformational changes, and using computer-aided predictions to assess druggability and target validity.

Identify the druggable proteome and its potential as novel molecular targets. Learn how computational methods, docking, simulations, and machine learning aid discovery.

Use the Swiss target prediction web server to input a molecule and reveal human protein targets, such as cyclooxygenase one, a transporter, and interleukin eight, with binding probabilities.

Explore the Therapeutic Target Database, a curated collection of validated target proteins and DNA across diseases, offering detailed information and versatile search by target, disease, drug, biomarkers, or scaffold.

Explore the therapeutic target database part 6 to identify disease targets using ICD codes, and access detailed target and drug info, expression profiles, and clinical status.

Unpack structural bioinformatics and its tools to assess 3d protein structures, explain the sequence–structure–function relationship, and focus on predicting protein–ligand interactions for drug discovery.

Explore the distinct architectures of proteins, nucleic acids, lipids, and carbohydrates, with a focus on protein structures as primary drug discovery targets and their role as cell workhorses.

Proteins perform diverse functions—regulation, catalysis, structural roles, signaling, transport, channels, and immune responses—whose 3D structures reveal mechanisms and disease roles, guiding drug discovery.

Explore the hierarchy of protein structure from primary sequence through secondary and tertiary to quaternary assemblies, highlighting amino acids, folding, and multi-subunit function for drug design.

Explore how amino acids link via peptide bonds to form proteins, examine torsional angles phi, psi, and omega that drive folding, and use internal coordinates to assess structure quality.

Explore how alpha helices, beta strands, and beta sheets contribute to protein stability and shape through hydrogen bonding; discuss turns, loops, and their roles in folding.

X-ray crystallography reveals atomic protein structures by turning diffraction patterns into electron density maps; higher resolution improves atom placement, aided by NMR and cryo-EM for 3D coordinate visualization.

Discover how researchers obtain biomolecular structures from the Protein Data Bank, represented in multiple entries and isoforms (bound or unbound), with PDB format used for storage.

Explore the Protein Data Bank and its diverse protein structures, and learn how PDB files encode atoms, coordinates, occupancy, and B factors for visualization.

Explore how visualization schemes translate x, y, z coordinates into intuitive protein structures, showcasing line, stick, ball-and-stick, CPK, cartoon, and surface representations to reveal secondary structures and binding regions.

Visualize protein structures from downloaded data, translate, rotate, and scale to explore different angles and regions, align structures for comparison, and perform structural similarity searches to identify similar proteins.

Visualize drug target interactions using structural data to understand ligand binding, compare protein structures, assess mutations, and guide molecular docking in computer-aided drug design.

Learn to visualize molecular structures using multiple representations—ribbon, stick, ball-and-stick, and sphere—color by hydrophobicity, show interactions, and label atoms, then explore the Protein Data Bank.

Explore the protein data bank to access high-quality protein structures, learn advanced search filters by organism, method, and resolution, and select ligand-bound entries for accurate docking and modeling.